




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__            = logging.get_logger(__name__)

lowerCamelCase__            = {
    '''facebook/detr-resnet-50''': '''https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json''',
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class 		_lowerCAmelCase       (							__lowerCAmelCase	):
			"""simple docstring"""
			lowerCAmelCase__           ='''detr'''
			lowerCAmelCase__           =['''past_key_values''']
			lowerCAmelCase__           ={
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			}
			def __init__(					self     ,		__SCREAMING_SNAKE_CASE=True     ,		__SCREAMING_SNAKE_CASE=None     ,		__SCREAMING_SNAKE_CASE=3     ,		__SCREAMING_SNAKE_CASE=100     ,		__SCREAMING_SNAKE_CASE=6     ,		__SCREAMING_SNAKE_CASE=2048     ,		__SCREAMING_SNAKE_CASE=8     ,		__SCREAMING_SNAKE_CASE=6     ,		__SCREAMING_SNAKE_CASE=2048     ,		__SCREAMING_SNAKE_CASE=8     ,		__SCREAMING_SNAKE_CASE=0.0     ,		__SCREAMING_SNAKE_CASE=0.0     ,		__SCREAMING_SNAKE_CASE=True     ,		__SCREAMING_SNAKE_CASE="relu"     ,		__SCREAMING_SNAKE_CASE=256     ,		__SCREAMING_SNAKE_CASE=0.1     ,		__SCREAMING_SNAKE_CASE=0.0     ,		__SCREAMING_SNAKE_CASE=0.0     ,		__SCREAMING_SNAKE_CASE=0.02     ,		__SCREAMING_SNAKE_CASE=1.0     ,		__SCREAMING_SNAKE_CASE=False     ,		__SCREAMING_SNAKE_CASE="sine"     ,		__SCREAMING_SNAKE_CASE="resnet50"     ,		__SCREAMING_SNAKE_CASE=True     ,		__SCREAMING_SNAKE_CASE=False     ,		__SCREAMING_SNAKE_CASE=1     ,		__SCREAMING_SNAKE_CASE=5     ,		__SCREAMING_SNAKE_CASE=2     ,		__SCREAMING_SNAKE_CASE=1     ,		__SCREAMING_SNAKE_CASE=1     ,		__SCREAMING_SNAKE_CASE=5     ,		__SCREAMING_SNAKE_CASE=2     ,		__SCREAMING_SNAKE_CASE=0.1     ,		**__SCREAMING_SNAKE_CASE     ,		)		->							Optional[Any]:





				"""simple docstring"""







				if backbone_config is not None and use_timm_backbone:
					raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''       )

				if not use_timm_backbone:
					if backbone_config is None:
						logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
						snake_case__						:     Union[str, Any]		=CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
					elif isinstance(lowerCamelCase_     ,		lowerCamelCase_       ):
						snake_case__						:     str		=backbone_config.get('''model_type'''       )
						snake_case__						:     Optional[int]		=CONFIG_MAPPING[backbone_model_type]
						snake_case__						:     str		=config_class.from_dict(lowerCamelCase_       )
					# set timm attributes to None
					snake_case__,    snake_case__,    snake_case__						:     Dict		=None, None, None

				snake_case__						:     int		=use_timm_backbone
				snake_case__						:     Tuple		=backbone_config
				snake_case__						:     Tuple		=num_channels
				snake_case__						:     Any		=num_queries
				snake_case__						:     List[Any]		=d_model
				snake_case__						:     Union[str, Any]		=encoder_ffn_dim
				snake_case__						:     int		=encoder_layers
				snake_case__						:     List[str]		=encoder_attention_heads
				snake_case__						:     Optional[Any]		=decoder_ffn_dim
				snake_case__						:     Any		=decoder_layers
				snake_case__						:     int		=decoder_attention_heads
				snake_case__						:     str		=dropout
				snake_case__						:     Union[str, Any]		=attention_dropout
				snake_case__						:     Any		=activation_dropout
				snake_case__						:     Union[str, Any]		=activation_function
				snake_case__						:     Tuple		=init_std
				snake_case__						:     Dict		=init_xavier_std
				snake_case__						:     Optional[int]		=encoder_layerdrop
				snake_case__						:     Any		=decoder_layerdrop
				snake_case__						:     List[Any]		=encoder_layers
				snake_case__						:     str		=auxiliary_loss
				snake_case__						:     Optional[int]		=position_embedding_type
				snake_case__						:     Optional[Any]		=backbone
				snake_case__						:     str		=use_pretrained_backbone
				snake_case__						:     Tuple		=dilation
				# Hungarian matcher
				snake_case__						:     List[Any]		=class_cost
				snake_case__						:     str		=bbox_cost
				snake_case__						:     List[str]		=giou_cost
				# Loss coefficients
				snake_case__						:     Dict		=mask_loss_coefficient
				snake_case__						:     Tuple		=dice_loss_coefficient
				snake_case__						:     Optional[Any]		=bbox_loss_coefficient
				snake_case__						:     List[str]		=giou_loss_coefficient
				snake_case__						:     str		=eos_coefficient
				super().__init__(is_encoder_decoder=lowerCamelCase_     ,		**lowerCamelCase_       )
			@property
			def 					UpperCAmelCase     (					self       )		->							Dict:





				"""simple docstring"""







				return self.encoder_attention_heads
			@property
			def 					UpperCAmelCase     (					self       )		->							Dict:





				"""simple docstring"""







				return self.d_model
			@classmethod
			def 					UpperCAmelCase     (					cls     ,		__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE       )		->							List[Any]:





				"""simple docstring"""







				return cls(backbone_config=lowerCamelCase_     ,		**lowerCamelCase_       )


			def 					UpperCAmelCase     (					self       )		->							List[str]:





				"""simple docstring"""







				snake_case__						:     List[Any]		=copy.deepcopy(self.__dict__       )
				if output["backbone_config"] is not None:
					snake_case__						:     Optional[int]		=self.backbone_config.to_dict()
				snake_case__						:     Tuple		=self.__class__.model_type
				return output






class 		_lowerCAmelCase       (							__lowerCAmelCase	):
			"""simple docstring"""
			lowerCAmelCase__           =version.parse('''1.11'''	)
			@property
			def 					UpperCAmelCase     (					self       )		->							Optional[Any]:





				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				        ('''pixel_mask''', {0: '''batch'''}),
				    ]       )
			@property
			def 					UpperCAmelCase     (					self       )		->							List[Any]:





				"""simple docstring"""







				return 1e-5


			@property
			def 					UpperCAmelCase     (					self       )		->							str:





				"""simple docstring"""







				return 12





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


_SCREAMING_SNAKE_CASE 		=      ["""text""", """image""", """audio"""]





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for input_type in input_types:
  if input_type == "text":
   inputs.append("""Text input"""       )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       ).resize((512, 512)       )       )
  elif input_type == "audio":
   inputs.append(torch.ones(3000       )       )
  elif isinstance(UpperCamelCase_							,					UpperCamelCase_       ):
   inputs.append(create_inputs(UpperCamelCase_       )       )
  else:
   raise ValueError(f"""Invalid type requested: {input_type}"""       )

 return inputs





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for output in outputs:
  if isinstance(UpperCamelCase_							,					(str, AgentText)       ):
   output_types.append("""text"""       )
  elif isinstance(UpperCamelCase_							,					(Image.Image, AgentImage)       ):
   output_types.append("""image"""       )
  elif isinstance(UpperCamelCase_							,					(torch.Tensor, AgentAudio)       ):
   output_types.append("""audio"""       )
  else:
   raise ValueError(f"""Invalid output: {output}"""       )

 return output_types


@is_tool_test
class SCREAMING_SNAKE_CASE_		:







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """inputs"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """outputs"""				)				)

    UpperCamelCase			  =      self.tool.inputs
    for _input in inputs:
     if isinstance(_input   ,   lowerCamelCase_				):
      for __input in _input:
       self.assertTrue(__input in authorized_types				)
     else:
      self.assertTrue(_input in authorized_types				)

    UpperCamelCase			  =      self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    # There is a single output
    if len(self.tool.outputs				) == 1:
     UpperCamelCase			  =      [outputs]

    self.assertListEqual(output_types(lowerCamelCase_				)   ,   self.tool.outputs				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """description"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """default_checkpoint"""				)				)
    self.assertTrue(self.tool.description.startswith("""This is a tool that"""				)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)

    for output, output_type in zip(lowerCamelCase_   ,   self.tool.outputs				):
     UpperCamelCase			  =      AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)

    UpperCamelCase			  =      []

    for _input, input_type in zip(lowerCamelCase_   ,   self.tool.inputs				):
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)
